Overview

Dataset statistics

Number of variables13
Number of observations104
Missing cells64
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory73.2 B

Variable types

TimeSeries11
DateTime1
Categorical1

Alerts

Active Truck Utilization (SA) is highly overall correlated with Total TL: Spot Rate (exc. FSC, SA) and 1 other fieldsHigh correlation
Total Truck Loadings (SA) is highly overall correlated with Total TL: Spot Rate (exc. FSC, SA) and 6 other fieldsHigh correlation
Total TL: Spot Rate (exc. FSC, SA) is highly overall correlated with Active Truck Utilization (SA) and 5 other fieldsHigh correlation
Total TL: Contract Rate (exc. FSC, SA) is highly overall correlated with Total Truck Loadings (SA) and 6 other fieldsHigh correlation
Driver Labor Index (1992=100, SA) is highly overall correlated with Total Truck Loadings (SA) and 5 other fieldsHigh correlation
Truck Driver Pressure Index (0 = Neutral, SA) is highly overall correlated with Active Truck Utilization (SA)High correlation
Real GDP is highly overall correlated with Total Truck Loadings (SA) and 6 other fieldsHigh correlation
CPI Index is highly overall correlated with Total Truck Loadings (SA) and 6 other fieldsHigh correlation
3 Month T-Bill Rate, % is highly overall correlated with Total Truck Loadings (SA) and 1 other fieldsHigh correlation
National Avg. Diesel Fuel Price ($/Gal.) is highly overall correlated with Driver Labor Index (1992=100, SA) and 3 other fieldsHigh correlation
Year is highly overall correlated with Total Truck Loadings (SA) and 6 other fieldsHigh correlation
Total TL: Spot Rate (exc. FSC, SA) has 32 (30.8%) missing valuesMissing
Total TL: Contract Rate (exc. FSC, SA) has 32 (30.8%) missing valuesMissing
Total Truck Loadings (SA) is non stationaryNon stationary
Total TL: Spot Rate (exc. FSC, SA) is non stationaryNon stationary
Total TL: Contract Rate (exc. FSC, SA) is non stationaryNon stationary
Driver Labor Index (1992=100, SA) is non stationaryNon stationary
Real GDP is non stationaryNon stationary
CPI Index is non stationaryNon stationary
National Avg. Diesel Fuel Price ($/Gal.) is non stationaryNon stationary
Year is non stationaryNon stationary
Quarter is uniformly distributedUniform
Total Truck Loadings (SA) has unique valuesUnique
Driver Labor Index (1992=100, SA) has unique valuesUnique
Truck Driver Pressure Index (0 = Neutral, SA) has unique valuesUnique
Real GDP has unique valuesUnique
CPI Index has unique valuesUnique
National Avg. Diesel Fuel Price ($/Gal.) has unique valuesUnique
Date has unique valuesUnique

Reproduction

Analysis started2023-05-01 22:26:08.769250
Analysis finished2023-05-01 22:26:26.003515
Duration17.23 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Distinct99
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90979063
Minimum0.81875342
Maximum1
Zeros0
Zeros (%)0.0%
Memory size544.0 B
2023-05-01T15:26:26.069173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.81875342
5-th percentile0.84976066
Q10.87812315
median0.90228552
Q30.93613689
95-th percentile0.99958504
Maximum1
Range0.18124658
Interquartile range (IQR)0.058013737

Descriptive statistics

Standard deviation0.044866666
Coefficient of variation (CV)0.049315375
Kurtosis-0.52721417
Mean0.90979063
Median Absolute Deviation (MAD)0.0301148
Skewness0.44197157
Sum94.618226
Variance0.0020130179
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.001255926517
2023-05-01T15:26:26.216973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6
 
5.8%
0.8955511451 1
 
1.0%
0.8767299652 1
 
1.0%
0.9147752523 1
 
1.0%
0.952093184 1
 
1.0%
0.9832124114 1
 
1.0%
0.9874635339 1
 
1.0%
0.9677611589 1
 
1.0%
0.9315921664 1
 
1.0%
0.8658621907 1
 
1.0%
Other values (89) 89
85.6%
ValueCountFrequency (%)
0.8187534213 1
1.0%
0.828115344 1
1.0%
0.8424383402 1
1.0%
0.8449956775 1
1.0%
0.8459328413 1
1.0%
0.849678874 1
1.0%
0.8502241373 1
1.0%
0.8508908153 1
1.0%
0.8510677814 1
1.0%
0.8517776132 1
1.0%
ValueCountFrequency (%)
1 6
5.8%
0.9972336292 1
 
1.0%
0.9968987107 1
 
1.0%
0.9932938218 1
 
1.0%
0.9874635339 1
 
1.0%
0.9832124114 1
 
1.0%
0.9775281549 1
 
1.0%
0.9677611589 1
 
1.0%
0.9638091326 1
 
1.0%
0.9638071656 1
 
1.0%
2023-05-01T15:26:26.326582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Total Truck Loadings (SA)
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7392279 × 108
Minimum1.4217122 × 108
Maximum1.9923736 × 108
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2023-05-01T15:26:26.537299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.4217122 × 108
5-th percentile1.5106549 × 108
Q11.6262954 × 108
median1.7155669 × 108
Q31.9095913 × 108
95-th percentile1.9687628 × 108
Maximum1.9923736 × 108
Range57066141
Interquartile range (IQR)28329586

Descriptive statistics

Standard deviation15772884
Coefficient of variation (CV)0.090688998
Kurtosis-1.2090462
Mean1.7392279 × 108
Median Absolute Deviation (MAD)13993580
Skewness0.031799012
Sum1.8087971 × 1010
Variance2.4878387 × 1014
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.8782398002
2023-05-01T15:26:26.681097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166834222.9 1
 
1.0%
168695651.8 1
 
1.0%
192367136.8 1
 
1.0%
191365664.9 1
 
1.0%
191147265.7 1
 
1.0%
191293145.7 1
 
1.0%
190321071.9 1
 
1.0%
190896417.7 1
 
1.0%
187795405.1 1
 
1.0%
187063791 1
 
1.0%
Other values (94) 94
90.4%
ValueCountFrequency (%)
142171223.1 1
1.0%
144844283.9 1
1.0%
145609965 1
1.0%
145958760.8 1
1.0%
149739215.2 1
1.0%
150792516.4 1
1.0%
152612313.4 1
1.0%
152832222.1 1
1.0%
152865448.6 1
1.0%
153261879 1
1.0%
ValueCountFrequency (%)
199237364.1 1
1.0%
199089774.9 1
1.0%
198890804.9 1
1.0%
198000980.3 1
1.0%
197351692.2 1
1.0%
196971718.7 1
1.0%
196335490.8 1
1.0%
196133989.9 1
1.0%
195511981.7 1
1.0%
194944432.8 1
1.0%
2023-05-01T15:26:26.788352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Total TL: Spot Rate (exc. FSC, SA)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct72
Distinct (%)100.0%
Missing32
Missing (%)30.8%
Infinite0
Infinite (%)0.0%
Mean112.67607
Minimum77.902023
Maximum162.4313
Zeros0
Zeros (%)0.0%
Memory size544.0 B
2023-05-01T15:26:26.987703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum77.902023
5-th percentile85.267386
Q1103.12238
median108.9407
Q3119.991
95-th percentile149.67843
Maximum162.4313
Range84.529282
Interquartile range (IQR)16.868616

Descriptive statistics

Standard deviation17.677216
Coefficient of variation (CV)0.15688526
Kurtosis0.89831018
Mean112.67607
Median Absolute Deviation (MAD)7.5061531
Skewness0.80815655
Sum8112.6774
Variance312.48395
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3826696339
2023-05-01T15:26:27.102807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102.6251831 1
 
1.0%
145.5853577 1
 
1.0%
142.4832306 1
 
1.0%
129.2870789 1
 
1.0%
96.45489502 1
 
1.0%
109.3403091 1
 
1.0%
105.4861298 1
 
1.0%
104.580986 1
 
1.0%
101.0910568 1
 
1.0%
113.515213 1
 
1.0%
Other values (62) 62
59.6%
(Missing) 32
30.8%
ValueCountFrequency (%)
77.90202332 1
1.0%
80.17908478 1
1.0%
81.82704163 1
1.0%
82.68315125 1
1.0%
87.38175964 1
1.0%
93.94298553 1
1.0%
95.59709167 1
1.0%
95.89161682 1
1.0%
96.11296844 1
1.0%
96.45489502 1
1.0%
ValueCountFrequency (%)
162.4313049 1
1.0%
157.1585999 1
1.0%
157.1009979 1
1.0%
154.681076 1
1.0%
145.5853577 1
1.0%
142.4832306 1
1.0%
135.1296234 1
1.0%
135.0182037 1
1.0%
134.4703217 1
1.0%
131.2262726 1
1.0%
2023-05-01T15:26:27.204956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Total TL: Contract Rate (exc. FSC, SA)
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct72
Distinct (%)100.0%
Missing32
Missing (%)30.8%
Infinite0
Infinite (%)0.0%
Mean120.85154
Minimum95.654587
Maximum156.7821
Zeros0
Zeros (%)0.0%
Memory size544.0 B
2023-05-01T15:26:27.421796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum95.654587
5-th percentile96.518226
Q1105.69841
median114.89983
Q3139.07974
95-th percentile152.066
Maximum156.7821
Range61.127518
Interquartile range (IQR)33.381336

Descriptive statistics

Standard deviation18.20997
Coefficient of variation (CV)0.1506805
Kurtosis-1.0775963
Mean120.85154
Median Absolute Deviation (MAD)12.947575
Skewness0.431725
Sum8701.3107
Variance331.60306
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.976873648
2023-05-01T15:26:27.545716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113.3463745 1
 
1.0%
135.536911 1
 
1.0%
132.0414886 1
 
1.0%
126.2863235 1
 
1.0%
120.8508377 1
 
1.0%
124.3693542 1
 
1.0%
125.0056305 1
 
1.0%
125.241539 1
 
1.0%
126.0108719 1
 
1.0%
128.6747131 1
 
1.0%
Other values (62) 62
59.6%
(Missing) 32
30.8%
ValueCountFrequency (%)
95.65458679 1
1.0%
96.18639374 1
1.0%
96.26087189 1
1.0%
96.37526703 1
1.0%
96.63519287 1
1.0%
97.5608902 1
1.0%
98.60038757 1
1.0%
100 1
1.0%
100.1637192 1
1.0%
100.5591888 1
1.0%
ValueCountFrequency (%)
156.7821045 1
1.0%
155.7073364 1
1.0%
154.4971771 1
1.0%
152.3362732 1
1.0%
151.8448639 1
1.0%
150.3144073 1
1.0%
149.0257721 1
1.0%
147.7256775 1
1.0%
146.0992279 1
1.0%
145.7658234 1
1.0%
2023-05-01T15:26:27.646373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Driver Labor Index (1992=100, SA)
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.87068
Minimum105.24831
Maximum130.13795
Zeros0
Zeros (%)0.0%
Memory size544.0 B
2023-05-01T15:26:27.858729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum105.24831
5-th percentile106.67719
Q1113.00985
median124.5961
Q3125.62868
95-th percentile129.58436
Maximum130.13795
Range24.889648
Interquartile range (IQR)12.618832

Descriptive statistics

Standard deviation7.3772297
Coefficient of variation (CV)0.06103407
Kurtosis-0.86102539
Mean120.87068
Median Absolute Deviation (MAD)2.1377258
Skewness-0.78133309
Sum12570.551
Variance54.423515
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.2942684524
2023-05-01T15:26:27.981258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105.3871918 1
 
1.0%
105.4735794 1
 
1.0%
126.3891525 1
 
1.0%
126.3680191 1
 
1.0%
125.6259308 1
 
1.0%
125.8481369 1
 
1.0%
125.7602539 1
 
1.0%
125.0952835 1
 
1.0%
125.6369476 1
 
1.0%
125.3000641 1
 
1.0%
Other values (94) 94
90.4%
ValueCountFrequency (%)
105.2483063 1
1.0%
105.3871918 1
1.0%
105.4735794 1
1.0%
105.7556076 1
1.0%
106.4534607 1
1.0%
106.620018 1
1.0%
107.001152 1
1.0%
107.9843445 1
1.0%
108.4228821 1
1.0%
109.2613678 1
1.0%
ValueCountFrequency (%)
130.1379547 1
1.0%
130.0317841 1
1.0%
129.9294891 1
1.0%
129.8338623 1
1.0%
129.7434235 1
1.0%
129.6118164 1
1.0%
129.4287872 1
1.0%
129.1701508 1
1.0%
128.8356934 1
1.0%
128.3970947 1
1.0%
2023-05-01T15:26:28.085813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Truck Driver Pressure Index (0 = Neutral, SA)
Numeric time series

HIGH CORRELATION  UNIQUE 

Distinct104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087947926
Minimum-11.97446
Maximum11.560026
Zeros0
Zeros (%)0.0%
Memory size544.0 B
2023-05-01T15:26:28.298152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-11.97446
5-th percentile-9.4072257
Q1-3.7261863
median-1.0079513
Q34.4764653
95-th percentile9.5077991
Maximum11.560026
Range23.534486
Interquartile range (IQR)8.2026517

Descriptive statistics

Standard deviation5.7012801
Coefficient of variation (CV)64.825634
Kurtosis-0.64832276
Mean0.087947926
Median Absolute Deviation (MAD)4.1142559
Skewness0.064632796
Sum9.1465843
Variance32.504593
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.001419702713
2023-05-01T15:26:28.414689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.296499372 1
 
1.0%
3.378000021 1
 
1.0%
-3.124495268 1
 
1.0%
-0.1671283692 1
 
1.0%
4.410934925 1
 
1.0%
8.168843269 1
 
1.0%
10.71324921 1
 
1.0%
10.46580029 1
 
1.0%
11.26981544 1
 
1.0%
11.56002617 1
 
1.0%
Other values (94) 94
90.4%
ValueCountFrequency (%)
-11.97445965 1
1.0%
-11.75789738 1
1.0%
-11.24374199 1
1.0%
-10.8213501 1
1.0%
-9.601939201 1
1.0%
-9.476758003 1
1.0%
-9.013209343 1
1.0%
-8.931984901 1
1.0%
-7.21913147 1
1.0%
-7.036275864 1
1.0%
ValueCountFrequency (%)
11.56002617 1
1.0%
11.26981544 1
1.0%
10.71324921 1
1.0%
10.46580029 1
1.0%
9.982379913 1
1.0%
9.524805069 1
1.0%
9.411432266 1
1.0%
8.962598801 1
1.0%
8.783802032 1
1.0%
8.387318611 1
1.0%
2023-05-01T15:26:28.514977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Real GDP
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16797.25
Minimum12935.252
Maximum21040.896
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2023-05-01T15:26:28.736006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12935.252
5-th percentile13265.645
Q115209.354
median16370.76
Q318766.643
95-th percentile20512.542
Maximum21040.896
Range8105.6445
Interquartile range (IQR)3557.2891

Descriptive statistics

Standard deviation2332.8303
Coefficient of variation (CV)0.13888169
Kurtosis-1.0889578
Mean16797.25
Median Absolute Deviation (MAD)1761.1995
Skewness0.17402813
Sum1746914
Variance5442097.4
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9640674595
2023-05-01T15:26:29.229776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12935.252 1
 
1.0%
13170.749 1
 
1.0%
18835.41016 1
 
1.0%
18733.74023 1
 
1.0%
18699.74805 1
 
1.0%
18565.69727 1
 
1.0%
18437.12695 1
 
1.0%
18310.3 1
 
1.0%
18127.994 1
 
1.0%
17979.218 1
 
1.0%
Other values (94) 94
90.4%
ValueCountFrequency (%)
12935.252 1
1.0%
13170.749 1
1.0%
13183.89 1
1.0%
13219.251 1
1.0%
13248.142 1
1.0%
13262.25 1
1.0%
13284.881 1
1.0%
13301.394 1
1.0%
13394.91 1
1.0%
13477.356 1
1.0%
ValueCountFrequency (%)
21040.89648 1
1.0%
20933.94531 1
1.0%
20828.23828 1
1.0%
20724.76172 1
1.0%
20621.99414 1
1.0%
20525.94141 1
1.0%
20436.61523 1
1.0%
20356.19141 1
1.0%
20294.55273 1
1.0%
20280.16992 1
1.0%
2023-05-01T15:26:29.357115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

CPI Index
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3421304
Minimum1.701
Maximum3.3580749
Zeros0
Zeros (%)0.0%
Memory size544.0 B
2023-05-01T15:26:29.561764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.701
5-th percentile1.7718833
Q12.0206667
median2.3170717
Q32.555185
95-th percentile3.2254039
Maximum3.3580749
Range1.6570749
Interquartile range (IQR)0.5345183

Descriptive statistics

Standard deviation0.43130147
Coefficient of variation (CV)0.18414921
Kurtosis-0.19792463
Mean2.3421304
Median Absolute Deviation (MAD)0.26494479
Skewness0.65959543
Sum243.58156
Variance0.18602096
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9990185836
2023-05-01T15:26:29.695847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.700999975 1
 
1.0%
1.714333296 1
 
1.0%
2.532927036 1
 
1.0%
2.526900053 1
 
1.0%
2.517703056 1
 
1.0%
2.50680995 1
 
1.0%
2.492542982 1
 
1.0%
2.472383261 1
 
1.0%
2.452869892 1
 
1.0%
2.441200018 1
 
1.0%
Other values (94) 94
90.4%
ValueCountFrequency (%)
1.700999975 1
1.0%
1.714333296 1
1.0%
1.730000019 1
1.0%
1.742333293 1
1.0%
1.758999944 1
1.0%
1.771333337 1
1.0%
1.774999976 1
1.0%
1.776333332 1
1.0%
1.780666709 1
1.0%
1.794666648 1
1.0%
ValueCountFrequency (%)
3.358074903 1
1.0%
3.335891962 1
1.0%
3.312577963 1
1.0%
3.287779093 1
1.0%
3.260850906 1
1.0%
3.23054409 1
1.0%
3.196275949 1
1.0%
3.159301043 1
1.0%
3.120743036 1
1.0%
3.082120895 1
1.0%
2023-05-01T15:26:29.819748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

3 Month T-Bill Rate, %
Numeric time series

Distinct95
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9191265
Minimum0.013333334
Maximum6.1999998
Zeros0
Zeros (%)0.0%
Memory size544.0 B
2023-05-01T15:26:30.023532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.013333334
5-th percentile0.026666717
Q10.12333325
median1.2016667
Q33.501532
95-th percentile5.4116821
Maximum6.1999998
Range6.1866665
Interquartile range (IQR)3.3781988

Descriptive statistics

Standard deviation1.9707756
Coefficient of variation (CV)1.0269128
Kurtosis-0.90098035
Mean1.9191265
Median Absolute Deviation (MAD)1.1466666
Skewness0.72713441
Sum199.58916
Variance3.8839567
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.01017973497
2023-05-01T15:26:30.143448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08666666597 3
 
2.9%
0.03333333507 2
 
1.9%
0.04666666687 2
 
1.9%
0.05000000075 2
 
1.9%
0.1266666651 2
 
1.9%
0.1566666663 2
 
1.9%
0.02333333343 2
 
1.9%
0.02666666731 2
 
1.9%
0.9033333063 1
 
1.0%
2.436666965 1
 
1.0%
Other values (85) 85
81.7%
ValueCountFrequency (%)
0.01333333366 1
1.0%
0.01999999955 1
1.0%
0.02333333343 2
1.9%
0.02666666731 2
1.9%
0.02666700073 1
1.0%
0.03333333507 2
1.9%
0.03999999911 1
1.0%
0.04666666687 2
1.9%
0.04666699842 1
1.0%
0.05000000075 2
1.9%
ValueCountFrequency (%)
6.199999809 1
1.0%
6.196666718 1
1.0%
5.889999866 1
1.0%
5.696666718 1
1.0%
5.419120789 1
1.0%
5.414663792 1
1.0%
5.394785881 1
1.0%
5.321630955 1
1.0%
5.186318874 1
1.0%
5.116666794 1
1.0%
2023-05-01T15:26:30.250241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

National Avg. Diesel Fuel Price ($/Gal.)
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9753201
Minimum1.1783333
Maximum5.4816666
Zeros0
Zeros (%)0.0%
Memory size544.0 B
2023-05-01T15:26:30.445247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.1783333
5-th percentile1.42265
Q12.3688334
median2.919
Q33.8899166
95-th percentile4.3701493
Maximum5.4816666
Range4.3033333
Interquartile range (IQR)1.5210832

Descriptive statistics

Standard deviation0.9978072
Coefficient of variation (CV)0.3353613
Kurtosis-0.67677826
Mean2.9753201
Median Absolute Deviation (MAD)0.8471669
Skewness0.03376333
Sum309.43329
Variance0.99561924
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1414207189
2023-05-01T15:26:30.557241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.432000041 1
 
1.0%
1.421000004 1
 
1.0%
3.017666578 1
 
1.0%
3.262666702 1
 
1.0%
3.237666607 1
 
1.0%
3.197666645 1
 
1.0%
3.017333269 1
 
1.0%
2.870666742 1
 
1.0%
2.625333309 1
 
1.0%
2.551333427 1
 
1.0%
Other values (94) 94
90.4%
ValueCountFrequency (%)
1.178333282 1
1.0%
1.258000016 1
1.0%
1.299999952 1
1.0%
1.345999956 1
1.0%
1.419999957 1
1.0%
1.421000004 1
1.0%
1.432000041 1
1.0%
1.437000036 1
1.0%
1.463000059 1
1.0%
1.466666698 1
1.0%
ValueCountFrequency (%)
5.481666565 1
1.0%
5.164000034 1
1.0%
5.059999943 1
1.0%
4.521440506 1
1.0%
4.39533329 1
1.0%
4.374940395 1
1.0%
4.342999935 1
1.0%
4.287000179 1
1.0%
4.121421814 1
1.0%
4.096830368 1
1.0%
2023-05-01T15:26:30.654016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Date
Date

Distinct104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size960.0 B
Minimum2000-01-01 00:00:00
Maximum2025-10-01 00:00:00
2023-05-01T15:26:30.872926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:31.000866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Year
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct26
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.5
Minimum2000
Maximum2025
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2023-05-01T15:26:31.128346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12006
median2012.5
Q32019
95-th percentile2024
Maximum2025
Range25
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.5363198
Coefficient of variation (CV)0.0037447552
Kurtosis-1.2034996
Mean2012.5
Median Absolute Deviation (MAD)6.5
Skewness0
Sum209300
Variance56.796117
MonotonicityIncreasing
Augmented Dickey-Fuller test p-value0.9491214515
2023-05-01T15:26:31.232761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2000 4
 
3.8%
2001 4
 
3.8%
2024 4
 
3.8%
2023 4
 
3.8%
2022 4
 
3.8%
2021 4
 
3.8%
2020 4
 
3.8%
2019 4
 
3.8%
2018 4
 
3.8%
2017 4
 
3.8%
Other values (16) 64
61.5%
ValueCountFrequency (%)
2000 4
3.8%
2001 4
3.8%
2002 4
3.8%
2003 4
3.8%
2004 4
3.8%
2005 4
3.8%
2006 4
3.8%
2007 4
3.8%
2008 4
3.8%
2009 4
3.8%
ValueCountFrequency (%)
2025 4
3.8%
2024 4
3.8%
2023 4
3.8%
2022 4
3.8%
2021 4
3.8%
2020 4
3.8%
2019 4
3.8%
2018 4
3.8%
2017 4
3.8%
2016 4
3.8%
2023-05-01T15:26:31.322056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Quarter
Categorical

Distinct4
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size960.0 B
1
26 
2
26 
3
26 
4
26 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters104
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row1

Common Values

ValueCountFrequency (%)
1 26
25.0%
2 26
25.0%
3 26
25.0%
4 26
25.0%

Length

2023-05-01T15:26:31.516742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T15:26:31.617035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 26
25.0%
2 26
25.0%
3 26
25.0%
4 26
25.0%

Most occurring characters

ValueCountFrequency (%)
1 26
25.0%
2 26
25.0%
3 26
25.0%
4 26
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 104
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 26
25.0%
2 26
25.0%
3 26
25.0%
4 26
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 104
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 26
25.0%
2 26
25.0%
3 26
25.0%
4 26
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26
25.0%
2 26
25.0%
3 26
25.0%
4 26
25.0%

Interactions

2023-05-01T15:26:24.239750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:09.315889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:10.628086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:12.009730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:13.309874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:14.776910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:16.308695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:18.063510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:19.725370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:21.769596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:23.013049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:24.357844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:09.439174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:10.753579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:12.126626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:13.425763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:14.903820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:16.478177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:18.228858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:19.905430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:21.891201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:23.135157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:24.469671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:09.560438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:10.870556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:12.273698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:13.536907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:15.027384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:16.671656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:18.397212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:20.041633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:22.008731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:23.248934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:24.577165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:09.666722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:10.975488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:12.379627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:13.640271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:15.160327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:16.819803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:18.527349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:20.857031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:22.113296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:23.370514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:24.689652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:09.782839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:11.115224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:12.487239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:13.749018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:15.307112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:16.962660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:18.674535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:20.969297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:22.233105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:23.487999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:24.798420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:09.893609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:11.272695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:12.607486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:13.900348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:15.441374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:17.127148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:18.815266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:21.094355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:22.346619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:23.593598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:24.899132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:10.006114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:11.407590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:12.728396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:14.023749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:15.576279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:17.258342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:18.948504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:21.199590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:22.451788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:23.699133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:25.017820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:10.162653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:11.549718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:12.858937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:14.144509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:15.730826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:17.455115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:19.078299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:21.321903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:22.577070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:23.819803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:25.127121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:10.282532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:11.669553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:12.972219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:14.257805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:15.871993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:17.629130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:19.231003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:21.433972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:22.690993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:23.928913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:25.235807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:10.401263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:11.786072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:13.075103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:14.422829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:16.011036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:17.781718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:19.395977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:21.548472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:22.800935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:24.033608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:25.344449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:10.513404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:11.895860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:13.195123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:14.629456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:16.167575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:17.917217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:19.561699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:21.654288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:22.905587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T15:26:24.135165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-01T15:26:31.702595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Active Truck Utilization (SA)Total Truck Loadings (SA)Total TL: Spot Rate (exc. FSC, SA)Total TL: Contract Rate (exc. FSC, SA)Driver Labor Index (1992=100, SA)Truck Driver Pressure Index (0 = Neutral, SA)Real GDPCPI Index3 Month T-Bill Rate, %National Avg. Diesel Fuel Price ($/Gal.)YearQuarter
Active Truck Utilization (SA)1.0000.1610.5050.205-0.1320.951-0.095-0.108-0.105-0.224-0.1130.000
Total Truck Loadings (SA)0.1611.0000.6770.9510.527-0.0270.6760.6410.5120.3090.6360.000
Total TL: Spot Rate (exc. FSC, SA)0.5050.6771.0000.7190.3650.3080.6460.6370.1410.4100.6130.000
Total TL: Contract Rate (exc. FSC, SA)0.2050.9510.7191.0000.723-0.0440.9660.9660.5070.3590.9610.000
Driver Labor Index (1992=100, SA)-0.1320.5270.3650.7231.000-0.2680.9240.930-0.0560.7460.9310.000
Truck Driver Pressure Index (0 = Neutral, SA)0.951-0.0270.308-0.044-0.2681.000-0.257-0.266-0.195-0.352-0.2680.107
Real GDP-0.0950.6760.6460.9660.924-0.2571.0000.993-0.0650.7190.9910.000
CPI Index-0.1080.6410.6370.9660.930-0.2660.9931.000-0.1020.7080.9980.000
3 Month T-Bill Rate, %-0.1050.5120.1410.507-0.056-0.195-0.065-0.1021.000-0.099-0.1050.000
National Avg. Diesel Fuel Price ($/Gal.)-0.2240.3090.4100.3590.746-0.3520.7190.708-0.0991.0000.6960.000
Year-0.1130.6360.6130.9610.931-0.2680.9910.998-0.1050.6961.0000.000
Quarter0.0000.0000.0000.0000.0000.1070.0000.0000.0000.0000.0001.000

Missing values

2023-05-01T15:26:25.508970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-01T15:26:25.758463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-01T15:26:25.928050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 1Active Truck Utilization (SA)Total Truck Loadings (SA)Total TL: Spot Rate (exc. FSC, SA)Total TL: Contract Rate (exc. FSC, SA)Driver Labor Index (1992=100, SA)Truck Driver Pressure Index (0 = Neutral, SA)Real GDPCPI Index3 Month T-Bill Rate, %National Avg. Diesel Fuel Price ($/Gal.)DateYearQuarter
00.8955511.668342e+08NaNNaN105.3871921.29649912935.2521.7010005.6966671.4320002000-01-0120001
10.9195421.686957e+08NaNNaN105.4735793.37800013170.7491.7143335.8900001.4210002000-04-0120002
20.9232291.683871e+08NaNNaN105.2483063.54635013183.8901.7300006.2000001.5123332000-07-0120003
30.9264811.696478e+08NaNNaN105.7556083.25227013262.2501.7423336.1966671.6076672000-10-0120004
40.9245001.667753e+08NaNNaN106.6200182.93117313219.2511.7590004.9466671.4716672001-01-0120011
50.9251961.648092e+08NaNNaN106.4534612.20964513301.3941.7713333.7466671.4666672001-04-0120012
60.9074811.614440e+08NaNNaN107.0011521.06998913248.1421.7763333.2400001.4200002001-07-0120013
70.9093561.615363e+08NaNNaN107.9843441.23745313284.8811.7750001.9433331.2580002001-10-0120014
80.9139631.602071e+08NaNNaN108.4228822.91689813394.9101.7806671.7566671.1783332002-01-0120021
90.9415731.639121e+08NaNNaN109.2613685.21906313477.3561.7946671.7466671.3000002002-04-0120022
Unnamed: 1Active Truck Utilization (SA)Total Truck Loadings (SA)Total TL: Spot Rate (exc. FSC, SA)Total TL: Contract Rate (exc. FSC, SA)Driver Labor Index (1992=100, SA)Truck Driver Pressure Index (0 = Neutral, SA)Real GDPCPI Index3 Month T-Bill Rate, %National Avg. Diesel Fuel Price ($/Gal.)DateYearQuarter
940.8449961.928742e+08106.492844140.144791128.397095-11.75789720280.1699223.0821215.4146644.1214222023-07-0120233
950.8424381.925154e+08109.079544139.054153128.835693-11.97446020294.5527343.1207435.4191214.0312752023-10-0120234
960.8459331.926422e+08111.320610139.156509129.170151-11.24374220356.1914063.1593015.3947863.8435822024-01-0120241
970.8564891.935778e+08113.312904140.114548129.428787-9.60193920436.6152343.1962765.3216313.9164332024-04-0120242
980.8721651.949444e+08116.754303141.287262129.611816-7.21913120525.9414063.2305445.0174253.9684252024-07-0120243
990.8897351.963355e+08123.345222143.374680129.743423-4.74919820621.9941413.2608514.6227344.0618172024-10-0120244
1000.9039091.980010e+08130.674347146.099228129.833862-2.94880120724.7617193.2877794.2661204.0634472025-01-0120251
1010.9137621.988908e+08134.470322149.025772129.929489-1.88524120828.2382813.3125783.8834684.0968302025-04-0120252
1020.9166091.992374e+08135.129623151.844864130.031784-1.82629120933.9453133.3358923.5861284.0655332025-07-0120253
1030.9073501.990898e+08135.018204154.497177130.137955-2.64668321040.8964843.3580753.3421384.0648082025-10-0120254